Lauren Yee
March 31, 2020
Course Materials: www.mapdatascience.com/ggplot
Wearer of many hats! Data Wrangling, Data Visualization, Modelling, Dashboards, Web Development and Research.
Prior to consulting role, studied emerging infectious diseases and spatial epidemiology
Lauren Yee Data Scientist
What this course is:
An introduction to the ggplot2 library and the various chart types and aesthetics
An introduction to R Markdown and reproducible reports
What this course is not:
a deep dive into data analysis or exploratory data analysis
a course on statistics, modelling or prediction
a comprehensive on all tidyverse functions and packages
Communicate complex information, spot patterns and to tell a story
Core component of Explortatory Data Analysis
Step taken before modelling or interpreting data
Highlight useful information and trends
Charts, graphs, infographics and maps
Human are perceptive at viewing and processing visual information for large amounts of data
Text and tables can be overwhelming and patterns are undetected
What question need to be answered?
Who is your intended audience ?
What are you trying to show ?
How can a visualization show this or make relationships more clear?
Too much information : many labels, too much colour, too much to interpret, many figures/data/labels on one plot
Confusing
What is the point?
Misleading
cutting off data values
bad scale or adjusted scales
hiding or removing outliers when they may be important to the anaylsis
Source: Economist https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368
John Snow, 1854
Source: Bill Rankin
Source: reddit.com/r/dataisbeautiful user:TrustLittleBrother